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3+ years experience with Distributed data/computing tools (MapReduce, Hadoop, Hive, EMR, Kafka, Spark, Gurobi, or MySQL) Utilize programming languages like Java, Scala, Python and Open Source RDBMS and NoSQL databases and Cloud based data warehousing services such as Redshift and Snowflake.
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Minimum of 5 years of hands-on skills in Python, Apache Spark, Hive, Cassandra, Snowflake, Elastic, Lucene, DataBricks, Hadoop, Redshift, Scala, Java, or other relevant technologies in data engineering.
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Familiarity with Hadoop, Spark, and Cloud databases (Snowflake, BigQuery, Redshift, Databricks) and stream processing frameworks (Kafka, Kinesis). Familiarity with Hadoop, Spark, and Cloud databases (Snowflake, BigQuery, Redshift, Databricks) and stream processing frameworks (Kafka, Kinesis.
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2+ years of data warehousing experience (Redshift or Snowflake) 2+ years of experience with a public cloud (AWS, Microsoft Azure, Google Cloud) 5+ years of experience in application development including Python, SQL, Scala, or Java.
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Comfortable with open-source technologies including Apache Spark, Hadoop, and Kafka. Strong development experience in at least one of the following technologies: Java/Python/Scala/Springboot Hadoop/Spark/Angular.
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Work with a team of developers with deep experience in machine learning, distributed microservices, and full stack systems. As a Capital One Data Engineer, you’ll have the opportunity to be on the forefront of driving a major transformation within Capital One.
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Strong understanding of data processing technologies and distributed computing frameworks (e.g. Hadoop, Hive, Airflow, Spark, DBT, Kafka, Flink) Hands-on experience with database systems (e.g. SQL, NoSQL, vector, search) and data warehousing solutions (e.g. Snowflake, Redshift, BigQuery.
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Nice to have: Experience with AWS; Kubernetes / Docker; Jenkins; Hadoop/Spark; Kibana; Kafka; NiFi; ElasticSearch; NoSQL / MongoDB / document-oriented DB; AI/ML understanding Bachelors degree Security Clearance: Active TS/SCI with Polygraph Compensation for roles at Accenture Federal Services varies depending on a wide array of factors including but not limited to the specific office location, role, skill set and level of experience.
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Design, develop, and maintain high volume Java and Scala based data processing jobs using industry standard tools and frameworks in the Hadoop ecosystem, such as Spark, Kafka, Hive, Impala, Avro, Flume, Oozie, and Sqoop.
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You have a deep understanding of data modeling and experience with data engineering tools and platforms such as Kafka, Spark, and Hadoop. and any of the distributed processing platforms like Hadoop, Spark, Hive, Oozie, and Airflow in a production setting.
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Experience with Google Cloud Platform cloud services including BigQuery, Cloud Composer, Dataflow, CloudSQL, GCS, Cloud Functions, and Pub/Sub. Description: STG is a SEI CMMi Level 5 company with several Fortune 500 and State Government clients.
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5+ years hands-on experience with the Big Data stack (HDFS, SPARK, MapReduce, Hadoop, Sqoop, Pig, Hive, Hbase, Flume, Kafka) Create information solutions covering data security, data privacy, metadata management, multi-tenancy and mixed workload management across Hadoop and NoSQL platforms, spanning on-premise and Cloud based deployments.
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Experience with big data technologies like Trino, Spark, Hadoop, Airflow, Kafka. Our current customers include Square, Aflac, Rippling, Compass, Buzzfeed, Barstool Sports, PBS, Planetscale, and Starburst.
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MUST HAVE: Hadoop, Spark, Cloud, Python/PySpark and Java, Streaming, Kafka, Backend experience to be considered. What you'll bring:Must HaveWell versed with Hadoop, Spark, Cloud, Python/PySpark and Java, Streaming, Kafka, Backend.
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Hands on in Hadoop Ecosystem (Kafka, Spark, Scala, Hive) is a plus. Site reliability engineering (SRE) fuses the software engineering and operations disciplines. Knowledge/Experience in Core Java, GoLang, Rust,Python.
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